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Bayesian inference in generalized true random-effects model and Gibbs sampling

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  • Makieła, Kamil

Abstract

The paper investigates Bayesian approach to estimating generalized true random-effects model (GTRE) via Gibbs sampling. Simulation results show that under properly defined priors for transient and persistent inefficiency components the posterior characteristics of the GTRE model are well approximated using simple Gibbs sampling procedure. No model reparametrization is required and if such is made it leads to much lower numerical efficiency. The new model allows us to make more reasonable assumptions as regards prior inefficiency distribution and appears more reliable in handling especially nuisance datasets. Empirical application furthers the research into stochastic frontier analysis using GTRE by examining the relationship between inefficiency terms in GTRE, true random-effects (TRE), generalized stochastic frontier and a standard stochastic frontier model.

Suggested Citation

  • Makieła, Kamil, 2016. "Bayesian inference in generalized true random-effects model and Gibbs sampling," MPRA Paper 69389, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:69389
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    References listed on IDEAS

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    More about this item

    Keywords

    generalized true random-effects model; stochastic frontier analysis; Bayesian inference; cost efficiency; firm heterogeneity; transient and persistent efficiency;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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